Policy Search by Target Distribution Learning for Continuous Control

Understand

We observe that several existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may suffer from overly large gradients when the current policy is close to deterministic (even in some very simple environments), leading to an unstable training process.

• To address this issue, we propose a new method, called \emph{target distribution learning} (TDL), for policy improvement in reinforcement learning.
• TDL alternates between proposing a target distribution and training the policy network to approach the target distribution.
• TDL is more effective in constraining the KL divergence between updated policies, and hence leads to more stable policy improvements over iterations.